Claim · 2023-08 · publication · Stated in source · Supported

The EBA follow-up report of August 2023 on machine learning for IRB models states that the main challenge discussed for using machine-learning techniques in IRB models is their complexity, which leads to difficulties in explaining their results, traceability and adequate understanding by management functions.

One recorded claim. The claim panel gives its qualification, the source passages with their place in each source, and how it was checked.

What it connects

Machine learning for IRB models: follow-up report from the consultation on the discussion paper on machine learning for IRB models (EBA/REP/2023/28) → articulates → The opacity of AI models complicates risk management and supervision

Where it appears

AI and money: what central banks and financial authorities have said, 1997–2026 →

Claim and evidence

Machine learning for IRB models: follow-up report from the consultation on the discussion paper on machine learning for IRB models (EBA/REP/2023/28)articulates ↓The opacity of AI models complicates risk management and supervision

Machine-checkedStated in sourceSupported

Supporting evidence: primary · 1 source

Date: 2023-08 · publication Records dated 2023 →
Role: Articulation
Register (kind of text): Nonfiction

Scope and limitsA statement of what the text says, not evidence that the risk has materialised or that any authority has adopted a policy. The scope is internal ratings-based credit risk models of banks.

The evidence 01

primary sourceSupports the claim

Machine learning for IRB models: follow-up report from the consultation on the discussion paper on machine learning for IRB models (EBA/REP/2023/28)

European Banking Authority · 2023-08

Cite the original source

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Source excerpt

“The DP discussed as the main pivotal challenge for using ML techniques the complexity, which leads, to challenges in explaining their results. This goes hand in hand with the problem of traceability and ensuring an adequate understanding by the management functions.”

Location in source Section 1 “Executive Summary”, PDF p. 3

How it was read Retrieved directly from the publisher

Machine-checked · 2026-11-06

Source details and check notes

Authors: European Banking Authority
Version: Retrieved for the candidate pack on 2026-11-02 (SHA-256 2b0b4163180513fa04fc91ee705f1a9c7ff531ee1a3e537dff73d06c498d23e7). Retrieved again at admission on 2026-11-06 (same file). PDF dated August 2023 as served by eba.europa.eu on 2026-11-02
Canonical address: Open the canonical source ↗
Accessed: 2026-11-06
Language: English (en)
Attribution: European Banking Authority, “Machine learning for IRB models: follow-up report from the consultation on the discussion paper on machine learning for IRB models (EBA/REP/2023/28)”, 2023-08.
Reuse: No open redistribution licence asserted. Bibliographic metadata, editorial paraphrases and short attributed quotations (at most 50 words each). Recheck terms before wider redistribution.
Check notes: Candidate pack passage, AI and money (research/ai-money). The quotation (41 words) matched a fresh retrieval of 2026-11-06 (the same file the candidate pack read). Matching ignored differences of whitespace, case, quotation marks, dashes, line-break hyphens and Unicode compatibility forms. No human editorial review.

Status and meaning

Editorial status. Machine-checked. Machines found this record and checked it against its sources; no person accepted it individually. Each evidence record above shows how it was checked.

Relation. A passage of the document meets the proposition’s criterion. The record documents what the text states, in its register (fiction and satire are labelled); it is not evidence that the proposition is true and implies no influence on or from another text.

Date. Date of publication, as the text gives it.

Record ID am-u04-eba-2023-ml-irb